most citedBounds on Causal Effects and Application to High Dimensional Data

3 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI20221 cited

Probabilities of Causation: Role of Observational Data

Ang Li, Judea Pearl

Probabilities of causation play a crucial role in modern decision-making. Pearl defined three binary probabilities of causation, the probability of necessity and sufficiency (PNS),…

cs.AI20221 cited

Unit Selection: Learning Benefit Function from Finite Population Data

Ang Li, Song Jiang, Yizhou Sun +1

The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one…

cs.AI20221 cited

Unit Selection: Case Study and Comparison with A/B Test Heuristic

Ang Li, Judea Pearl

The unit selection problem defined by Li and Pearl identifies individuals who have desired counterfactual behavior patterns, for example, individuals who would respond positively i…

cs.AI2022

Probabilities of Causation: Adequate Size of Experimental and Observational Samples

Ang Li, Ruirui Mao, Judea Pearl

The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the…

stat.ME20213 cited

Bounds on Causal Effects and Application to High Dimensional Data

Ang Li, Judea Pearl

This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed. For such scenarios, we deri…

stat.ME2021

Causes of Effects: Learning individual responses from population data

Scott Mueller, Ang Li, Judea Pearl

The problem of individualization is recognized as crucial in almost every field. Identifying causes of effects in specific events is likewise essential for accurate decision making…